Overview

Dataset statistics

Number of variables28
Number of observations68894
Missing cells125773
Missing cells (%)6.5%
Duplicate rows15639
Duplicate rows (%)22.7%
Total size in memory14.7 MiB
Average record size in memory224.0 B

Variable types

Text1
Categorical5
Numeric17
Boolean5

Alerts

Dataset has 15639 (22.7%) duplicate rowsDuplicates
buildYear is highly overall correlated with buildingMaterial and 2 other fieldsHigh correlation
buildingMaterial is highly overall correlated with buildYear and 1 other fieldsHigh correlation
centreDistance is highly overall correlated with collegeDistance and 1 other fieldsHigh correlation
clinicDistance is highly overall correlated with poiCountHigh correlation
collegeDistance is highly overall correlated with centreDistance and 1 other fieldsHigh correlation
condition is highly overall correlated with buildYearHigh correlation
floor is highly overall correlated with floorCountHigh correlation
floorCount is highly overall correlated with floor and 1 other fieldsHigh correlation
hasElevator is highly overall correlated with floorCountHigh correlation
pharmacyDistance is highly overall correlated with poiCountHigh correlation
poiCount is highly overall correlated with centreDistance and 6 other fieldsHigh correlation
postOfficeDistance is highly overall correlated with poiCountHigh correlation
price is highly overall correlated with squareMetersHigh correlation
restaurantDistance is highly overall correlated with poiCountHigh correlation
rooms is highly overall correlated with squareMetersHigh correlation
schoolDistance is highly overall correlated with poiCountHigh correlation
squareMeters is highly overall correlated with price and 1 other fieldsHigh correlation
type is highly overall correlated with buildYear and 1 other fieldsHigh correlation
ownership is highly imbalanced (69.8%)Imbalance
hasSecurity is highly imbalanced (51.8%)Imbalance
type has 15278 (22.2%) missing valuesMissing
floor has 12472 (18.1%) missing valuesMissing
floorCount has 956 (1.4%) missing valuesMissing
buildYear has 11935 (17.3%) missing valuesMissing
collegeDistance has 2021 (2.9%) missing valuesMissing
buildingMaterial has 26791 (38.9%) missing valuesMissing
condition has 51924 (75.4%) missing valuesMissing
hasElevator has 3545 (5.1%) missing valuesMissing
poiCount has 3709 (5.4%) zerosZeros

Reproduction

Analysis started2023-12-16 08:56:04.135482
Analysis finished2023-12-16 08:56:49.733198
Duration45.6 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

id
Text

Distinct39028
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:49.858847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters2204608
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21736 ?
Unique (%)31.5%

Sample

1st rowf8524536d4b09a0c8ccc0197ec9d7bde
2nd rowaccbe77d4b360fea9735f138a50608dd
3rd row8373aa373dbc3fe7ca3b7434166b8766
4th row0a68cd14c44ec5140143ece75d739535
5th rowf66320e153c2441edc0fe293b54c8aeb
ValueCountFrequency (%)
98bd9e22e76cf1b940267b08127c69be 4
 
< 0.1%
8dcedc3348a412e1231409f709a878c0 4
 
< 0.1%
c2f6967522d51476018897b09eda1681 4
 
< 0.1%
f07d1a367ba574098a19051aac2b9420 4
 
< 0.1%
e9515cd3c41b08a685b7be771be6c87e 4
 
< 0.1%
a2c86da33af1985a6311b733ad66c484 4
 
< 0.1%
d43824e76ee9fcec2b56efa42f95cbd4 4
 
< 0.1%
e27d0f0fe4010f798fc6d988889ea99b 4
 
< 0.1%
847e663331e42081f83f3371b8f8d435 4
 
< 0.1%
32de3e2719ac695f6ebead4c3a1e47f3 4
 
< 0.1%
Other values (39018) 68854
99.9%
2023-12-16T09:56:50.114401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 138570
 
6.3%
a 138433
 
6.3%
2 138129
 
6.3%
6 138061
 
6.3%
3 138055
 
6.3%
0 137963
 
6.3%
5 137941
 
6.3%
e 137940
 
6.3%
d 137921
 
6.3%
7 137843
 
6.3%
Other values (6) 823752
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1377792
62.5%
Lowercase Letter 826816
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 138570
10.1%
2 138129
10.0%
6 138061
10.0%
3 138055
10.0%
0 137963
10.0%
5 137941
10.0%
7 137843
10.0%
8 137300
10.0%
1 137121
10.0%
9 136809
9.9%
Lowercase Letter
ValueCountFrequency (%)
a 138433
16.7%
e 137940
16.7%
d 137921
16.7%
f 137710
16.7%
c 137579
16.6%
b 137233
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1377792
62.5%
Latin 826816
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 138570
10.1%
2 138129
10.0%
6 138061
10.0%
3 138055
10.0%
0 137963
10.0%
5 137941
10.0%
7 137843
10.0%
8 137300
10.0%
1 137121
10.0%
9 136809
9.9%
Latin
ValueCountFrequency (%)
a 138433
16.7%
e 137940
16.7%
d 137921
16.7%
f 137710
16.7%
c 137579
16.6%
b 137233
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2204608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 138570
 
6.3%
a 138433
 
6.3%
2 138129
 
6.3%
6 138061
 
6.3%
3 138055
 
6.3%
0 137963
 
6.3%
5 137941
 
6.3%
e 137940
 
6.3%
d 137921
 
6.3%
7 137843
 
6.3%
Other values (6) 823752
37.4%

city
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size538.4 KiB
warszawa
20256 
krakow
9883 
gdansk
6888 
wroclaw
6612 
lodz
5559 
Other values (10)
19696 

Length

Max length11
Median length9
Mean length6.896377
Min length4

Characters and Unicode

Total characters475119
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowszczecin
2nd rowszczecin
3rd rowszczecin
4th rowszczecin
5th rowszczecin

Common Values

ValueCountFrequency (%)
warszawa 20256
29.4%
krakow 9883
14.3%
gdansk 6888
 
10.0%
wroclaw 6612
 
9.6%
lodz 5559
 
8.1%
bydgoszcz 3677
 
5.3%
gdynia 3000
 
4.4%
poznan 2818
 
4.1%
lublin 2318
 
3.4%
szczecin 2049
 
3.0%
Other values (5) 5834
 
8.5%

Length

2023-12-16T09:56:50.228242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
warszawa 20256
29.4%
krakow 9883
14.3%
gdansk 6888
 
10.0%
wroclaw 6612
 
9.6%
lodz 5559
 
8.1%
bydgoszcz 3677
 
5.3%
gdynia 3000
 
4.4%
poznan 2818
 
4.1%
lublin 2318
 
3.4%
szczecin 2049
 
3.0%
Other values (5) 5834
 
8.5%

Most occurring characters

ValueCountFrequency (%)
a 95076
20.0%
w 67106
14.1%
z 42359
8.9%
r 38760
8.2%
s 35482
 
7.5%
o 35203
 
7.4%
k 29659
 
6.2%
d 20406
 
4.3%
n 19891
 
4.2%
c 17967
 
3.8%
Other values (11) 73210
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 475119
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 95076
20.0%
w 67106
14.1%
z 42359
8.9%
r 38760
8.2%
s 35482
 
7.5%
o 35203
 
7.4%
k 29659
 
6.2%
d 20406
 
4.3%
n 19891
 
4.2%
c 17967
 
3.8%
Other values (11) 73210
15.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 475119
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 95076
20.0%
w 67106
14.1%
z 42359
8.9%
r 38760
8.2%
s 35482
 
7.5%
o 35203
 
7.4%
k 29659
 
6.2%
d 20406
 
4.3%
n 19891
 
4.2%
c 17967
 
3.8%
Other values (11) 73210
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 95076
20.0%
w 67106
14.1%
z 42359
8.9%
r 38760
8.2%
s 35482
 
7.5%
o 35203
 
7.4%
k 29659
 
6.2%
d 20406
 
4.3%
n 19891
 
4.2%
c 17967
 
3.8%
Other values (11) 73210
15.4%

type
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing15278
Missing (%)22.2%
Memory size538.4 KiB
blockOfFlats
32104 
tenement
10836 
apartmentBuilding
10676 

Length

Max length17
Median length12
Mean length12.187183
Min length8

Characters and Unicode

Total characters653428
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblockOfFlats
2nd rowblockOfFlats
3rd rowtenement
4th rowtenement
5th rowblockOfFlats

Common Values

ValueCountFrequency (%)
blockOfFlats 32104
46.6%
tenement 10836
 
15.7%
apartmentBuilding 10676
 
15.5%
(Missing) 15278
22.2%

Length

2023-12-16T09:56:50.321319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T09:56:50.409299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
blockofflats 32104
59.9%
tenement 10836
 
20.2%
apartmentbuilding 10676
 
19.9%

Most occurring characters

ValueCountFrequency (%)
t 75128
 
11.5%
l 74884
 
11.5%
a 53456
 
8.2%
e 43184
 
6.6%
n 43024
 
6.6%
b 32104
 
4.9%
F 32104
 
4.9%
s 32104
 
4.9%
f 32104
 
4.9%
O 32104
 
4.9%
Other values (11) 203232
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 578544
88.5%
Uppercase Letter 74884
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 75128
13.0%
l 74884
12.9%
a 53456
9.2%
e 43184
 
7.5%
n 43024
 
7.4%
b 32104
 
5.5%
s 32104
 
5.5%
f 32104
 
5.5%
k 32104
 
5.5%
c 32104
 
5.5%
Other values (8) 128348
22.2%
Uppercase Letter
ValueCountFrequency (%)
F 32104
42.9%
O 32104
42.9%
B 10676
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 653428
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 75128
 
11.5%
l 74884
 
11.5%
a 53456
 
8.2%
e 43184
 
6.6%
n 43024
 
6.6%
b 32104
 
4.9%
F 32104
 
4.9%
s 32104
 
4.9%
f 32104
 
4.9%
O 32104
 
4.9%
Other values (11) 203232
31.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 653428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 75128
 
11.5%
l 74884
 
11.5%
a 53456
 
8.2%
e 43184
 
6.6%
n 43024
 
6.6%
b 32104
 
4.9%
F 32104
 
4.9%
s 32104
 
4.9%
f 32104
 
4.9%
O 32104
 
4.9%
Other values (11) 203232
31.1%

squareMeters
Real number (ℝ)

HIGH CORRELATION 

Distinct5537
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.944435
Minimum25
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:50.499705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile31.65
Q144.4925
median55
Q369
95-th percentile100.2
Maximum150
Range125
Interquartile range (IQR)24.5075

Descriptive statistics

Standard deviation21.279593
Coefficient of variation (CV)0.36101106
Kurtosis1.7384349
Mean58.944435
Median Absolute Deviation (MAD)12
Skewness1.1567838
Sum4060917.9
Variance452.82109
MonotonicityNot monotonic
2023-12-16T09:56:50.611711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 897
 
1.3%
50 835
 
1.2%
48 817
 
1.2%
60 794
 
1.2%
53 756
 
1.1%
49 714
 
1.0%
47 713
 
1.0%
46 669
 
1.0%
64 668
 
1.0%
56 659
 
1.0%
Other values (5527) 61372
89.1%
ValueCountFrequency (%)
25 176
0.3%
25.01 1
 
< 0.1%
25.02 7
 
< 0.1%
25.03 7
 
< 0.1%
25.04 12
 
< 0.1%
25.07 3
 
< 0.1%
25.1 7
 
< 0.1%
25.11 3
 
< 0.1%
25.12 6
 
< 0.1%
25.13 6
 
< 0.1%
ValueCountFrequency (%)
150 14
< 0.1%
149.94 1
 
< 0.1%
149.86 3
 
< 0.1%
149.8 4
 
< 0.1%
149.7 4
 
< 0.1%
149.68 3
 
< 0.1%
149.2 11
< 0.1%
149.03 1
 
< 0.1%
149 7
< 0.1%
148.9 2
 
< 0.1%

rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6852121
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:50.697081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91164067
Coefficient of variation (CV)0.33950416
Kurtosis0.68775643
Mean2.6852121
Median Absolute Deviation (MAD)1
Skewness0.6342431
Sum184995
Variance0.8310887
MonotonicityNot monotonic
2023-12-16T09:56:50.775049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 27698
40.2%
3 26007
37.7%
4 8942
 
13.0%
1 3979
 
5.8%
5 1777
 
2.6%
6 491
 
0.7%
ValueCountFrequency (%)
1 3979
 
5.8%
2 27698
40.2%
3 26007
37.7%
4 8942
 
13.0%
5 1777
 
2.6%
6 491
 
0.7%
ValueCountFrequency (%)
6 491
 
0.7%
5 1777
 
2.6%
4 8942
 
13.0%
3 26007
37.7%
2 27698
40.2%
1 3979
 
5.8%

floor
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)< 0.1%
Missing12472
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean3.3054305
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:50.855742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum29
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5064866
Coefficient of variation (CV)0.75829353
Kurtosis7.2215435
Mean3.3054305
Median Absolute Deviation (MAD)1
Skewness2.1052539
Sum186499
Variance6.2824749
MonotonicityNot monotonic
2023-12-16T09:56:50.956291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 13505
19.6%
3 11895
17.3%
2 11760
17.1%
4 8581
12.5%
5 3139
 
4.6%
6 1757
 
2.6%
7 1468
 
2.1%
8 1239
 
1.8%
9 1049
 
1.5%
10 971
 
1.4%
Other values (14) 1058
 
1.5%
(Missing) 12472
18.1%
ValueCountFrequency (%)
1 13505
19.6%
2 11760
17.1%
3 11895
17.3%
4 8581
12.5%
5 3139
 
4.6%
6 1757
 
2.6%
7 1468
 
2.1%
8 1239
 
1.8%
9 1049
 
1.5%
10 971
 
1.4%
ValueCountFrequency (%)
29 11
 
< 0.1%
24 6
 
< 0.1%
23 3
 
< 0.1%
22 4
 
< 0.1%
20 9
 
< 0.1%
19 5
 
< 0.1%
18 12
 
< 0.1%
17 80
0.1%
16 21
 
< 0.1%
15 67
0.1%

floorCount
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)< 0.1%
Missing956
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean5.2211722
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:51.050488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile11
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2708622
Coefficient of variation (CV)0.62646126
Kurtosis4.8758178
Mean5.2211722
Median Absolute Deviation (MAD)1
Skewness1.8685816
Sum354716
Variance10.698539
MonotonicityNot monotonic
2023-12-16T09:56:51.140894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 22088
32.1%
3 12628
18.3%
5 6702
 
9.7%
10 6051
 
8.8%
2 5693
 
8.3%
6 3404
 
4.9%
7 2313
 
3.4%
8 1885
 
2.7%
11 1785
 
2.6%
1 1744
 
2.5%
Other values (19) 3645
 
5.3%
(Missing) 956
 
1.4%
ValueCountFrequency (%)
1 1744
 
2.5%
2 5693
 
8.3%
3 12628
18.3%
4 22088
32.1%
5 6702
 
9.7%
6 3404
 
4.9%
7 2313
 
3.4%
8 1885
 
2.7%
9 885
 
1.3%
10 6051
 
8.8%
ValueCountFrequency (%)
29 16
 
< 0.1%
28 9
 
< 0.1%
27 12
 
< 0.1%
26 10
 
< 0.1%
25 21
< 0.1%
24 47
0.1%
23 40
0.1%
22 38
0.1%
21 7
 
< 0.1%
20 35
0.1%

buildYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct158
Distinct (%)0.3%
Missing11935
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean1984.5865
Minimum1850
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:51.241972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1850
5-th percentile1911
Q11965
median1991
Q32014
95-th percentile2023
Maximum2023
Range173
Interquartile range (IQR)49

Descriptive statistics

Standard deviation34.058699
Coefficient of variation (CV)0.01716161
Kurtosis0.024824268
Mean1984.5865
Median Absolute Deviation (MAD)24
Skewness-0.87199821
Sum1.1304006 × 108
Variance1159.995
MonotonicityNot monotonic
2023-12-16T09:56:51.347348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 3259
 
4.7%
2023 2950
 
4.3%
1980 2544
 
3.7%
1970 2321
 
3.4%
1960 1517
 
2.2%
2021 1402
 
2.0%
1930 1288
 
1.9%
2017 1209
 
1.8%
2020 1189
 
1.7%
2016 1188
 
1.7%
Other values (148) 38092
55.3%
(Missing) 11935
 
17.3%
ValueCountFrequency (%)
1850 1
 
< 0.1%
1851 4
 
< 0.1%
1852 4
 
< 0.1%
1854 3
 
< 0.1%
1855 2
 
< 0.1%
1860 9
< 0.1%
1864 1
 
< 0.1%
1865 8
< 0.1%
1867 4
 
< 0.1%
1870 14
< 0.1%
ValueCountFrequency (%)
2023 2950
4.3%
2022 3259
4.7%
2021 1402
2.0%
2020 1189
 
1.7%
2019 1001
 
1.5%
2018 1174
 
1.7%
2017 1209
 
1.8%
2016 1188
 
1.7%
2015 807
 
1.2%
2014 743
 
1.1%

latitude
Real number (ℝ)

Distinct24170
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.045462
Minimum49.978999
Maximum54.60646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:51.452487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum49.978999
5-th percentile50.045351
Q151.114026
median52.195312
Q352.440563
95-th percentile54.4109
Maximum54.60646
Range4.6274606
Interquartile range (IQR)1.3265377

Descriptive statistics

Standard deviation1.339131
Coefficient of variation (CV)0.025730024
Kurtosis-0.65985297
Mean52.045462
Median Absolute Deviation (MAD)0.94199187
Skewness0.23624583
Sum3585620.1
Variance1.7932718
MonotonicityNot monotonic
2023-12-16T09:56:51.555653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.126 456
 
0.7%
52.194157 213
 
0.3%
52.2463 182
 
0.3%
52.2333751 171
 
0.2%
52.2296756 169
 
0.2%
52.1378544 143
 
0.2%
52.3289879 131
 
0.2%
53.1081 129
 
0.2%
53.1249837 129
 
0.2%
53.1212 116
 
0.2%
Other values (24160) 67055
97.3%
ValueCountFrequency (%)
49.9789994 1
 
< 0.1%
49.97911 2
 
< 0.1%
49.98135663 3
< 0.1%
49.98245549 4
< 0.1%
49.982778 5
< 0.1%
49.9837508 1
 
< 0.1%
49.9840353 1
 
< 0.1%
49.9841 2
 
< 0.1%
49.98428 1
 
< 0.1%
49.9846339 1
 
< 0.1%
ValueCountFrequency (%)
54.60646 3
< 0.1%
54.5846491 1
 
< 0.1%
54.58321 4
< 0.1%
54.58287 2
< 0.1%
54.5814973 1
 
< 0.1%
54.5809 1
 
< 0.1%
54.58087 1
 
< 0.1%
54.580868 2
< 0.1%
54.57976778 4
< 0.1%
54.5797535 1
 
< 0.1%

longitude
Real number (ℝ)

Distinct24803
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.501237
Minimum14.447127
Maximum23.207128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:51.655830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum14.447127
5-th percentile16.9139
Q118.52378
median19.899315
Q320.994734
95-th percentile22.03748
Maximum23.207128
Range8.7600013
Interquartile range (IQR)2.4709544

Descriptive statistics

Standard deviation1.7814886
Coefficient of variation (CV)0.091352595
Kurtosis0.11116218
Mean19.501237
Median Absolute Deviation (MAD)1.1625536
Skewness-0.54483539
Sum1343518.2
Variance3.1737016
MonotonicityNot monotonic
2023-12-16T09:56:51.755788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0079 455
 
0.7%
21.0346955 213
 
0.3%
21.0695 179
 
0.3%
20.9571835 171
 
0.2%
21.0122287 169
 
0.2%
21.0291229 143
 
0.2%
18.0497523 129
 
0.2%
18.0339 129
 
0.2%
21.0076793 127
 
0.2%
17.9925 116
 
0.2%
Other values (24793) 67063
97.3%
ValueCountFrequency (%)
14.4471272 7
< 0.1%
14.462282 2
 
< 0.1%
14.47483813 1
 
< 0.1%
14.47735 1
 
< 0.1%
14.4779792 3
 
< 0.1%
14.478 1
 
< 0.1%
14.4787373 2
 
< 0.1%
14.4807861 4
< 0.1%
14.481843 2
 
< 0.1%
14.4830325 9
< 0.1%
ValueCountFrequency (%)
23.2071285 4
< 0.1%
23.19946694 1
 
< 0.1%
23.19886858 1
 
< 0.1%
23.1983355 1
 
< 0.1%
23.1957006 4
< 0.1%
23.1953627 2
< 0.1%
23.1941 2
< 0.1%
23.193926 2
< 0.1%
23.1937936 1
 
< 0.1%
23.1927185 1
 
< 0.1%

centreDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1407
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3426075
Minimum0.02
Maximum16.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:51.853048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.65
Q11.99
median3.94
Q36.12
95-th percentile9.8
Maximum16.94
Range16.92
Interquartile range (IQR)4.13

Descriptive statistics

Standard deviation2.8738567
Coefficient of variation (CV)0.66178137
Kurtosis0.50167468
Mean4.3426075
Median Absolute Deviation (MAD)2.05
Skewness0.8363402
Sum299179.6
Variance8.2590524
MonotonicityNot monotonic
2023-12-16T09:56:51.961532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65 500
 
0.7%
4.68 289
 
0.4%
4.77 260
 
0.4%
3.16 253
 
0.4%
0.56 246
 
0.4%
3.29 242
 
0.4%
1.32 223
 
0.3%
0.63 220
 
0.3%
2.7 214
 
0.3%
4.04 202
 
0.3%
Other values (1397) 66245
96.2%
ValueCountFrequency (%)
0.02 4
 
< 0.1%
0.03 6
 
< 0.1%
0.04 21
< 0.1%
0.05 8
 
< 0.1%
0.06 2
 
< 0.1%
0.07 15
< 0.1%
0.08 10
< 0.1%
0.09 10
< 0.1%
0.1 15
< 0.1%
0.11 11
< 0.1%
ValueCountFrequency (%)
16.94 1
 
< 0.1%
16.92 2
 
< 0.1%
16.91 4
< 0.1%
16.82 6
< 0.1%
16.75 1
 
< 0.1%
16.62 2
 
< 0.1%
16.51 2
 
< 0.1%
16.47 4
< 0.1%
16.37 4
< 0.1%
16.36 5
< 0.1%

poiCount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct171
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.33746
Minimum0
Maximum208
Zeros3709
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:52.068436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q324
95-th percentile67
Maximum208
Range208
Interquartile range (IQR)17

Descriptive statistics

Standard deviation23.816947
Coefficient of variation (CV)1.1710876
Kurtosis10.374106
Mean20.33746
Median Absolute Deviation (MAD)8
Skewness2.8355661
Sum1401129
Variance567.24697
MonotonicityNot monotonic
2023-12-16T09:56:52.170146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3709
 
5.4%
9 2753
 
4.0%
12 2725
 
4.0%
7 2631
 
3.8%
6 2558
 
3.7%
11 2530
 
3.7%
8 2529
 
3.7%
10 2417
 
3.5%
4 2339
 
3.4%
3 2217
 
3.2%
Other values (161) 42486
61.7%
ValueCountFrequency (%)
0 3709
5.4%
1 2167
3.1%
2 1927
2.8%
3 2217
3.2%
4 2339
3.4%
5 2215
3.2%
6 2558
3.7%
7 2631
3.8%
8 2529
3.7%
9 2753
4.0%
ValueCountFrequency (%)
208 5
 
< 0.1%
206 8
 
< 0.1%
202 9
 
< 0.1%
196 5
 
< 0.1%
195 1
 
< 0.1%
194 18
< 0.1%
192 3
 
< 0.1%
190 1
 
< 0.1%
186 4
 
< 0.1%
177 32
< 0.1%

schoolDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct2014
Distinct (%)2.9%
Missing69
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.41678062
Minimum0.004
Maximum4.818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:52.271947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.081
Q10.175
median0.29
Q30.468
95-th percentile1.136
Maximum4.818
Range4.814
Interquartile range (IQR)0.293

Descriptive statistics

Standard deviation0.47985114
Coefficient of variation (CV)1.1513279
Kurtosis25.467309
Mean0.41678062
Median Absolute Deviation (MAD)0.133
Skewness4.3629937
Sum28684.926
Variance0.23025712
MonotonicityNot monotonic
2023-12-16T09:56:52.384339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 602
 
0.9%
0.243 327
 
0.5%
0.285 316
 
0.5%
0.324 309
 
0.4%
0.392 297
 
0.4%
0.301 295
 
0.4%
0.179 283
 
0.4%
0.214 275
 
0.4%
0.202 269
 
0.4%
0.18 256
 
0.4%
Other values (2004) 65596
95.2%
ValueCountFrequency (%)
0.004 4
 
< 0.1%
0.005 5
 
< 0.1%
0.006 8
< 0.1%
0.007 15
< 0.1%
0.008 3
 
< 0.1%
0.009 5
 
< 0.1%
0.01 4
 
< 0.1%
0.011 3
 
< 0.1%
0.012 1
 
< 0.1%
0.013 7
< 0.1%
ValueCountFrequency (%)
4.818 3
< 0.1%
4.797 1
 
< 0.1%
4.762 1
 
< 0.1%
4.74 1
 
< 0.1%
4.692 4
< 0.1%
4.689 3
< 0.1%
4.672 5
< 0.1%
4.658 2
 
< 0.1%
4.517 4
< 0.1%
4.51 2
 
< 0.1%

clinicDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct3770
Distinct (%)5.5%
Missing319
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.98396611
Minimum0.001
Maximum4.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:52.889267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.131
Q10.359
median0.681
Q31.255
95-th percentile3.0346
Maximum4.998
Range4.997
Interquartile range (IQR)0.896

Descriptive statistics

Standard deviation0.90590662
Coefficient of variation (CV)0.92066852
Kurtosis2.4827166
Mean0.98396611
Median Absolute Deviation (MAD)0.383
Skewness1.6665942
Sum67475.476
Variance0.8206668
MonotonicityNot monotonic
2023-12-16T09:56:53.004954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.205 501
 
0.7%
0.735 246
 
0.4%
0.131 236
 
0.3%
0.255 224
 
0.3%
0.417 215
 
0.3%
0.478 210
 
0.3%
0.326 205
 
0.3%
0.703 179
 
0.3%
0.313 177
 
0.3%
0.902 166
 
0.2%
Other values (3760) 66216
96.1%
(Missing) 319
 
0.5%
ValueCountFrequency (%)
0.001 3
 
< 0.1%
0.002 3
 
< 0.1%
0.004 1
 
< 0.1%
0.005 4
 
< 0.1%
0.006 2
 
< 0.1%
0.007 3
 
< 0.1%
0.008 4
 
< 0.1%
0.009 6
< 0.1%
0.011 6
< 0.1%
0.013 12
< 0.1%
ValueCountFrequency (%)
4.998 1
 
< 0.1%
4.996 1
 
< 0.1%
4.989 4
< 0.1%
4.982 1
 
< 0.1%
4.94 2
 
< 0.1%
4.933 6
< 0.1%
4.913 1
 
< 0.1%
4.872 4
< 0.1%
4.866 1
 
< 0.1%
4.789 2
 
< 0.1%

postOfficeDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct2223
Distinct (%)3.2%
Missing95
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.52319577
Minimum0.001
Maximum4.968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:53.113098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.094
Q10.239
median0.392
Q30.628
95-th percentile1.339
Maximum4.968
Range4.967
Interquartile range (IQR)0.389

Descriptive statistics

Standard deviation0.51398835
Coefficient of variation (CV)0.98240157
Kurtosis20.026905
Mean0.52319577
Median Absolute Deviation (MAD)0.179
Skewness3.7437325
Sum35995.346
Variance0.26418402
MonotonicityNot monotonic
2023-12-16T09:56:53.224059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.351 611
 
0.9%
0.531 243
 
0.4%
0.26 242
 
0.4%
0.783 230
 
0.3%
0.299 228
 
0.3%
0.182 214
 
0.3%
0.207 213
 
0.3%
0.353 208
 
0.3%
0.629 204
 
0.3%
0.449 196
 
0.3%
Other values (2213) 66210
96.1%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.002 1
 
< 0.1%
0.003 9
 
< 0.1%
0.004 4
 
< 0.1%
0.005 2
 
< 0.1%
0.006 9
 
< 0.1%
0.007 1
 
< 0.1%
0.008 8
 
< 0.1%
0.009 15
 
< 0.1%
0.01 100
0.1%
ValueCountFrequency (%)
4.968 1
 
< 0.1%
4.967 1
 
< 0.1%
4.905 5
< 0.1%
4.889 2
 
< 0.1%
4.801 4
< 0.1%
4.798 3
< 0.1%
4.759 3
< 0.1%
4.746 1
 
< 0.1%
4.745 1
 
< 0.1%
4.739 3
< 0.1%

kindergartenDistance
Real number (ℝ)

Distinct1814
Distinct (%)2.6%
Missing68
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.37627051
Minimum0.002
Maximum4.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:53.331737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.06625
Q10.158
median0.266
Q30.421
95-th percentile0.932
Maximum4.96
Range4.958
Interquartile range (IQR)0.263

Descriptive statistics

Standard deviation0.46329268
Coefficient of variation (CV)1.2312755
Kurtosis30.51375
Mean0.37627051
Median Absolute Deviation (MAD)0.124
Skewness4.8907027
Sum25897.194
Variance0.2146401
MonotonicityNot monotonic
2023-12-16T09:56:53.445067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.405 509
 
0.7%
0.119 354
 
0.5%
0.373 285
 
0.4%
0.216 281
 
0.4%
0.117 280
 
0.4%
0.274 276
 
0.4%
0.236 275
 
0.4%
0.172 264
 
0.4%
0.366 263
 
0.4%
0.177 261
 
0.4%
Other values (1804) 65778
95.5%
ValueCountFrequency (%)
0.002 7
 
< 0.1%
0.003 3
 
< 0.1%
0.004 14
< 0.1%
0.005 24
< 0.1%
0.006 17
< 0.1%
0.007 3
 
< 0.1%
0.008 4
 
< 0.1%
0.009 19
< 0.1%
0.01 6
 
< 0.1%
0.011 11
< 0.1%
ValueCountFrequency (%)
4.96 9
< 0.1%
4.698 1
 
< 0.1%
4.695 1
 
< 0.1%
4.68 3
 
< 0.1%
4.676 1
 
< 0.1%
4.662 4
< 0.1%
4.659 3
 
< 0.1%
4.608 5
< 0.1%
4.594 2
 
< 0.1%
4.465 2
 
< 0.1%

restaurantDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1914
Distinct (%)2.8%
Missing193
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.35726601
Minimum0.001
Maximum4.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:53.553304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.037
Q10.117
median0.234
Q30.416
95-th percentile1.01
Maximum4.985
Range4.984
Interquartile range (IQR)0.299

Descriptive statistics

Standard deviation0.48410919
Coefficient of variation (CV)1.3550385
Kurtosis31.124467
Mean0.35726601
Median Absolute Deviation (MAD)0.138
Skewness4.8398298
Sum24544.532
Variance0.2343617
MonotonicityNot monotonic
2023-12-16T09:56:53.667007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.058 596
 
0.9%
0.059 362
 
0.5%
0.162 351
 
0.5%
0.08 315
 
0.5%
0.235 289
 
0.4%
0.463 278
 
0.4%
0.123 271
 
0.4%
0.088 249
 
0.4%
0.139 249
 
0.4%
0.055 243
 
0.4%
Other values (1904) 65498
95.1%
ValueCountFrequency (%)
0.001 14
 
< 0.1%
0.002 11
 
< 0.1%
0.003 12
 
< 0.1%
0.004 63
0.1%
0.005 39
 
0.1%
0.006 29
 
< 0.1%
0.007 39
 
0.1%
0.008 73
0.1%
0.009 117
0.2%
0.01 72
0.1%
ValueCountFrequency (%)
4.985 5
< 0.1%
4.971 2
 
< 0.1%
4.928 4
< 0.1%
4.925 3
 
< 0.1%
4.806 9
< 0.1%
4.718 4
< 0.1%
4.717 2
 
< 0.1%
4.715 3
 
< 0.1%
4.702 4
< 0.1%
4.696 9
< 0.1%

collegeDistance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4448
Distinct (%)6.7%
Missing2021
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1.4498954
Minimum0.006
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:53.772625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.006
5-th percentile0.206
Q10.584
median1.121
Q32.07
95-th percentile3.751
Maximum5
Range4.994
Interquartile range (IQR)1.486

Descriptive statistics

Standard deviation1.106158
Coefficient of variation (CV)0.7629226
Kurtosis0.35470298
Mean1.4498954
Median Absolute Deviation (MAD)0.659
Skewness1.0187594
Sum96958.857
Variance1.2235855
MonotonicityNot monotonic
2023-12-16T09:56:53.883906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.622 480
 
0.7%
2.017 221
 
0.3%
0.229 211
 
0.3%
0.523 208
 
0.3%
1.121 192
 
0.3%
1.139 175
 
0.3%
0.35 165
 
0.2%
0.904 156
 
0.2%
0.501 145
 
0.2%
0.861 138
 
0.2%
Other values (4438) 64782
94.0%
(Missing) 2021
 
2.9%
ValueCountFrequency (%)
0.006 1
 
< 0.1%
0.007 5
< 0.1%
0.011 1
 
< 0.1%
0.013 4
< 0.1%
0.014 1
 
< 0.1%
0.015 1
 
< 0.1%
0.017 1
 
< 0.1%
0.018 3
< 0.1%
0.019 6
< 0.1%
0.021 4
< 0.1%
ValueCountFrequency (%)
5 3
< 0.1%
4.998 2
< 0.1%
4.997 2
< 0.1%
4.996 2
< 0.1%
4.993 1
 
< 0.1%
4.991 3
< 0.1%
4.988 2
< 0.1%
4.986 2
< 0.1%
4.984 1
 
< 0.1%
4.981 3
< 0.1%

pharmacyDistance
Real number (ℝ)

HIGH CORRELATION 

Distinct1904
Distinct (%)2.8%
Missing107
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.36644689
Minimum0.001
Maximum4.992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:53.996794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.056
Q10.145
median0.241
Q30.407
95-th percentile1.0117
Maximum4.992
Range4.991
Interquartile range (IQR)0.262

Descriptive statistics

Standard deviation0.47842463
Coefficient of variation (CV)1.305577
Kurtosis30.299117
Mean0.36644689
Median Absolute Deviation (MAD)0.117
Skewness4.8364327
Sum25206.782
Variance0.22889012
MonotonicityNot monotonic
2023-12-16T09:56:54.107359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.205 566
 
0.8%
0.142 430
 
0.6%
0.108 423
 
0.6%
0.155 373
 
0.5%
0.146 351
 
0.5%
0.259 319
 
0.5%
0.166 308
 
0.4%
0.157 304
 
0.4%
0.189 302
 
0.4%
0.164 258
 
0.4%
Other values (1894) 65153
94.6%
ValueCountFrequency (%)
0.001 4
 
< 0.1%
0.003 10
 
< 0.1%
0.004 22
< 0.1%
0.005 14
< 0.1%
0.006 20
< 0.1%
0.007 29
< 0.1%
0.008 20
< 0.1%
0.009 26
< 0.1%
0.01 17
< 0.1%
0.011 20
< 0.1%
ValueCountFrequency (%)
4.992 1
 
< 0.1%
4.955 1
 
< 0.1%
4.929 1
 
< 0.1%
4.861 5
< 0.1%
4.847 2
 
< 0.1%
4.802 4
< 0.1%
4.799 3
 
< 0.1%
4.655 4
< 0.1%
4.636 3
 
< 0.1%
4.618 9
< 0.1%

ownership
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size538.4 KiB
condominium
61814 
cooperative
7077 
udział
 
3

Length

Max length11
Median length11
Mean length10.999782
Min length6

Characters and Unicode

Total characters757819
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcondominium
2nd rowcooperative
3rd rowcondominium
4th rowcondominium
5th rowcondominium

Common Values

ValueCountFrequency (%)
condominium 61814
89.7%
cooperative 7077
 
10.3%
udział 3
 
< 0.1%

Length

2023-12-16T09:56:54.208197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T09:56:54.286563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
condominium 61814
89.7%
cooperative 7077
 
10.3%
udział 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 137782
18.2%
i 130708
17.2%
n 123628
16.3%
m 123628
16.3%
c 68891
9.1%
d 61817
8.2%
u 61817
8.2%
e 14154
 
1.9%
a 7080
 
0.9%
p 7077
 
0.9%
Other values (5) 21237
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 757819
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 137782
18.2%
i 130708
17.2%
n 123628
16.3%
m 123628
16.3%
c 68891
9.1%
d 61817
8.2%
u 61817
8.2%
e 14154
 
1.9%
a 7080
 
0.9%
p 7077
 
0.9%
Other values (5) 21237
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 757819
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 137782
18.2%
i 130708
17.2%
n 123628
16.3%
m 123628
16.3%
c 68891
9.1%
d 61817
8.2%
u 61817
8.2%
e 14154
 
1.9%
a 7080
 
0.9%
p 7077
 
0.9%
Other values (5) 21237
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 757816
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 137782
18.2%
i 130708
17.2%
n 123628
16.3%
m 123628
16.3%
c 68891
9.1%
d 61817
8.2%
u 61817
8.2%
e 14154
 
1.9%
a 7080
 
0.9%
p 7077
 
0.9%
Other values (4) 21234
 
2.8%
None
ValueCountFrequency (%)
Å‚ 3
100.0%

buildingMaterial
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing26791
Missing (%)38.9%
Memory size538.4 KiB
brick
32519 
concreteSlab
9584 

Length

Max length12
Median length5
Mean length6.5934256
Min length5

Characters and Unicode

Total characters277603
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconcreteSlab
2nd rowconcreteSlab
3rd rowbrick
4th rowbrick
5th rowconcreteSlab

Common Values

ValueCountFrequency (%)
brick 32519
47.2%
concreteSlab 9584
 
13.9%
(Missing) 26791
38.9%

Length

2023-12-16T09:56:54.380758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T09:56:54.467720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
brick 32519
77.2%
concreteslab 9584
 
22.8%

Most occurring characters

ValueCountFrequency (%)
c 51687
18.6%
b 42103
15.2%
r 42103
15.2%
i 32519
11.7%
k 32519
11.7%
e 19168
 
6.9%
o 9584
 
3.5%
n 9584
 
3.5%
t 9584
 
3.5%
S 9584
 
3.5%
Other values (2) 19168
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 268019
96.5%
Uppercase Letter 9584
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 51687
19.3%
b 42103
15.7%
r 42103
15.7%
i 32519
12.1%
k 32519
12.1%
e 19168
 
7.2%
o 9584
 
3.6%
n 9584
 
3.6%
t 9584
 
3.6%
l 9584
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S 9584
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 277603
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 51687
18.6%
b 42103
15.2%
r 42103
15.2%
i 32519
11.7%
k 32519
11.7%
e 19168
 
6.9%
o 9584
 
3.5%
n 9584
 
3.5%
t 9584
 
3.5%
S 9584
 
3.5%
Other values (2) 19168
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 277603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 51687
18.6%
b 42103
15.2%
r 42103
15.2%
i 32519
11.7%
k 32519
11.7%
e 19168
 
6.9%
o 9584
 
3.5%
n 9584
 
3.5%
t 9584
 
3.5%
S 9584
 
3.5%
Other values (2) 19168
 
6.9%

condition
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing51924
Missing (%)75.4%
Memory size538.4 KiB
premium
9351 
low
7619 

Length

Max length7
Median length7
Mean length5.2041249
Min length3

Characters and Unicode

Total characters88314
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow
2nd rowpremium
3rd rowlow
4th rowlow
5th rowpremium

Common Values

ValueCountFrequency (%)
premium 9351
 
13.6%
low 7619
 
11.1%
(Missing) 51924
75.4%

Length

2023-12-16T09:56:54.556332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T09:56:54.639755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
premium 9351
55.1%
low 7619
44.9%

Most occurring characters

ValueCountFrequency (%)
m 18702
21.2%
p 9351
10.6%
r 9351
10.6%
e 9351
10.6%
i 9351
10.6%
u 9351
10.6%
l 7619
8.6%
o 7619
8.6%
w 7619
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88314
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 18702
21.2%
p 9351
10.6%
r 9351
10.6%
e 9351
10.6%
i 9351
10.6%
u 9351
10.6%
l 7619
8.6%
o 7619
8.6%
w 7619
8.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 88314
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 18702
21.2%
p 9351
10.6%
r 9351
10.6%
e 9351
10.6%
i 9351
10.6%
u 9351
10.6%
l 7619
8.6%
o 7619
8.6%
w 7619
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 18702
21.2%
p 9351
10.6%
r 9351
10.6%
e 9351
10.6%
i 9351
10.6%
u 9351
10.6%
l 7619
8.6%
o 7619
8.6%
w 7619
8.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
False
50691 
True
18203 
ValueCountFrequency (%)
False 50691
73.6%
True 18203
 
26.4%
2023-12-16T09:56:54.709042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

hasBalcony
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
True
39118 
False
29776 
ValueCountFrequency (%)
True 39118
56.8%
False 29776
43.2%
2023-12-16T09:56:54.775880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

hasElevator
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3545
Missing (%)5.1%
Memory size134.7 KiB
False
34587 
True
30762 
(Missing)
3545 
ValueCountFrequency (%)
False 34587
50.2%
True 30762
44.7%
(Missing) 3545
 
5.1%
2023-12-16T09:56:54.843376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

hasSecurity
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
False
61709 
True
7185 
ValueCountFrequency (%)
False 61709
89.6%
True 7185
 
10.4%
2023-12-16T09:56:54.912203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
False
37641 
True
31253 
ValueCountFrequency (%)
False 37641
54.6%
True 31253
45.4%
2023-12-16T09:56:54.978591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

price
Real number (ℝ)

HIGH CORRELATION 

Distinct4409
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715184.82
Minimum150000
Maximum2500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.4 KiB
2023-12-16T09:56:55.076770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum150000
5-th percentile289000
Q1470000
median649000
Q3860000
95-th percentile1400000
Maximum2500000
Range2350000
Interquartile range (IQR)390000

Descriptive statistics

Standard deviation351569.33
Coefficient of variation (CV)0.49157829
Kurtosis2.969827
Mean715184.82
Median Absolute Deviation (MAD)193050
Skewness1.4425824
Sum4.9271943 × 1010
Variance1.2360099 × 1011
MonotonicityNot monotonic
2023-12-16T09:56:55.184447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
599000 1014
 
1.5%
699000 953
 
1.4%
799000 780
 
1.1%
499000 724
 
1.1%
650000 709
 
1.0%
750000 602
 
0.9%
399000 548
 
0.8%
899000 544
 
0.8%
550000 543
 
0.8%
649000 508
 
0.7%
Other values (4399) 61969
89.9%
ValueCountFrequency (%)
150000 2
< 0.1%
151200 1
 
< 0.1%
152620 1
 
< 0.1%
154040 1
 
< 0.1%
154900 4
< 0.1%
155000 2
< 0.1%
155424 1
 
< 0.1%
159900 4
< 0.1%
160000 2
< 0.1%
161040 1
 
< 0.1%
ValueCountFrequency (%)
2500000 37
0.1%
2499999 4
 
< 0.1%
2499000 10
 
< 0.1%
2490000 10
 
< 0.1%
2485350 4
 
< 0.1%
2484000 4
 
< 0.1%
2480000 7
 
< 0.1%
2463435 1
 
< 0.1%
2456000 1
 
< 0.1%
2450000 12
 
< 0.1%

Interactions

2023-12-16T09:56:47.496846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:26.423479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:27.783729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:29.115184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:30.347713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:31.610397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:33.055708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:34.271438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:35.663954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:36.994210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:38.205990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:39.431013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:40.916010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:42.165673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:43.403331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:44.666866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:46.232069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:47.568984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:26.586383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:27.858454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:29.188922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:30.423573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:31.830082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:33.127869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:34.342311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:35.741966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:37.070001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:38.277737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:39.504822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:40.992054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:42.242239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:43.481654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:44.750718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:46.308247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:47.638566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:26.658744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:27.931363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:29.260071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:30.498310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:31.909687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:33.199101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:34.412095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:35.821809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:37.141093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:38.352478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:39.574186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:41.063103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:42.313189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:43.554246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:44.826346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:46.378070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:47.710508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:26.735782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:28.003860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:29.330535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:30.571850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:31.987593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:33.275238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:34.485365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:35.902799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:37.213951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:38.427065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:39.887957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:41.137415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-12-16T09:56:39.290604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:40.771573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:42.020817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:43.258589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:44.512213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:46.094995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:47.354862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:48.664456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:27.712207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:29.047036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:30.276144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:31.538854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:32.974162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:34.203156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:35.596682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:36.916442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:38.140237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:39.361755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:40.841751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:42.095147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:43.331659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:44.590712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:46.163562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-16T09:56:47.426607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-16T09:56:55.282443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
buildYearbuildingMaterialcentreDistancecityclinicDistancecollegeDistanceconditionfloorfloorCounthasBalconyhasElevatorhasParkingSpacehasSecurityhasStorageRoomkindergartenDistancelatitudelongitudeownershippharmacyDistancepoiCountpostOfficeDistancepricerestaurantDistanceroomsschoolDistancesquareMeterstype
buildYear1.0000.7670.3680.1290.2530.3920.511-0.0040.1230.3260.4780.2700.2530.4880.155-0.0450.0460.2560.315-0.4360.3180.2410.2310.0550.4000.0640.716
buildingMaterial0.7671.0000.1250.2200.0720.0950.2540.2710.3820.1250.1090.1360.1610.262-0.130-0.0080.0360.367-0.059-0.053-0.043-0.3290.096-0.032-0.070-0.1760.587
centreDistance0.3680.1251.0000.1890.4330.5970.131-0.026-0.0290.1760.1330.1310.1490.1430.0800.0820.1910.0400.299-0.5190.2860.1570.3290.0810.3950.0780.299
city0.1290.2200.1891.000-0.053-0.0280.2270.0640.1450.1230.2610.1890.1740.188-0.194-0.2500.2100.133-0.0640.1170.0290.280-0.0550.016-0.0380.0540.184
clinicDistance0.2530.0720.433-0.0531.0000.4130.123-0.109-0.1750.1280.0420.0540.0650.0820.2070.043-0.1000.0410.371-0.5810.323-0.1120.4200.0450.3830.0240.174
collegeDistance0.3920.0950.597-0.0280.4131.0000.123-0.040-0.0450.1580.1170.0980.1040.1250.2020.082-0.0170.0300.339-0.6140.2900.0350.3640.0630.4300.0550.259
condition0.5110.2540.1310.2270.1230.1231.000-0.036-0.0280.0000.2300.2670.0280.2430.0630.001-0.0490.2150.146-0.1400.1360.3500.0620.0190.1700.0700.353
floor-0.0040.271-0.0260.064-0.109-0.040-0.0361.0000.5430.0510.3460.0330.0340.055-0.103-0.0000.0610.088-0.1190.110-0.081-0.027-0.082-0.041-0.095-0.0850.146
floorCount0.1230.382-0.0290.145-0.175-0.045-0.0280.5431.0000.1270.5340.0930.0980.169-0.163-0.0390.1160.160-0.1550.168-0.1020.044-0.140-0.087-0.106-0.1480.264
hasBalcony0.3260.1250.1760.1230.1280.1580.0000.0510.1271.0000.1090.0760.0620.0990.003-0.0510.0620.0610.073-0.1540.0830.0970.0960.1180.1150.1150.345
hasElevator0.4780.1090.1330.2610.0420.1170.2300.3460.5340.1091.0000.1190.1340.270-0.016-0.0180.1270.0150.037-0.0340.0670.222-0.028-0.0490.107-0.0610.456
hasParkingSpace0.2700.1360.1310.1890.0540.0980.2670.0330.0930.0760.1191.0000.0880.0290.030-0.1130.0350.0050.078-0.0900.0830.1670.0410.0740.0980.1240.224
hasSecurity0.2530.1610.1490.1740.0650.1040.0280.0340.0980.0620.1340.0881.0000.1160.0480.0490.0690.0740.080-0.0810.0860.1630.0320.0300.1080.0690.192
hasStorageRoom0.4880.2620.1430.1880.0820.1250.2430.0550.1690.0990.2700.0290.1161.000-0.1070.005-0.0290.165-0.1200.122-0.123-0.182-0.0220.018-0.174-0.0170.324
kindergartenDistance0.155-0.1300.080-0.1940.2070.2020.063-0.103-0.1630.003-0.0160.0300.048-0.1071.0000.104-0.1570.0500.342-0.4150.2810.0060.2450.0480.3750.0690.076
latitude-0.045-0.0080.082-0.2500.0430.0820.001-0.000-0.039-0.051-0.018-0.1130.0490.0050.1041.000-0.2120.1060.039-0.0600.013-0.0040.0290.0250.0620.0260.142
longitude0.0460.0360.1910.210-0.100-0.017-0.0490.0610.1160.0620.1270.0350.069-0.029-0.157-0.2121.0000.111-0.0460.113-0.0150.209-0.1220.010-0.053-0.0040.147
ownership0.2560.3670.0400.1330.0410.0300.2150.0880.1600.0610.0150.0050.0740.1650.0500.1060.1111.000-0.0690.036-0.057-0.1340.0060.015-0.080-0.0330.171
pharmacyDistance0.315-0.0590.299-0.0640.3710.3390.146-0.119-0.1550.0730.0370.0780.080-0.1200.3420.039-0.046-0.0691.000-0.6120.4670.0140.4770.0440.4030.0410.117
poiCount-0.436-0.053-0.5190.117-0.581-0.614-0.1400.1100.168-0.154-0.034-0.090-0.0810.122-0.415-0.0600.1130.036-0.6121.000-0.5620.093-0.690-0.065-0.603-0.0490.304
postOfficeDistance0.318-0.0430.2860.0290.3230.2900.136-0.081-0.1020.0830.0670.0830.086-0.1230.2810.013-0.015-0.0570.467-0.5621.0000.0090.4300.0310.3910.0330.158
price0.241-0.3290.1570.280-0.1120.0350.350-0.0270.0440.0970.2220.1670.163-0.1820.006-0.0040.209-0.1340.0140.0930.0091.000-0.1560.4870.0760.6080.246
restaurantDistance0.2310.0960.329-0.0550.4200.3640.062-0.082-0.1400.096-0.0280.0410.032-0.0220.2450.029-0.1220.0060.477-0.6900.430-0.1561.0000.0460.3440.0190.098
rooms0.055-0.0320.0810.0160.0450.0630.019-0.041-0.0870.118-0.0490.0740.0300.0180.0480.0250.0100.0150.044-0.0650.0310.4870.0461.0000.0650.8270.092
schoolDistance0.400-0.0700.395-0.0380.3830.4300.170-0.095-0.1060.1150.1070.0980.108-0.1740.3750.062-0.053-0.0800.403-0.6030.3910.0760.3440.0651.0000.0810.165
squareMeters0.064-0.1760.0780.0540.0240.0550.070-0.085-0.1480.115-0.0610.1240.069-0.0170.0690.026-0.004-0.0330.041-0.0490.0330.6080.0190.8270.0811.0000.147
type0.7160.5870.2990.1840.1740.2590.3530.1460.2640.3450.4560.2240.1920.3240.0760.1420.1470.1710.1170.3040.1580.2460.0980.0920.1650.1471.000

Missing values

2023-12-16T09:56:48.813451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T09:56:49.145393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-16T09:56:49.517702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idcitytypesquareMetersroomsfloorfloorCountbuildYearlatitudelongitudecentreDistancepoiCountschoolDistanceclinicDistancepostOfficeDistancekindergartenDistancerestaurantDistancecollegeDistancepharmacyDistanceownershipbuildingMaterialconditionhasParkingSpacehasBalconyhasElevatorhasSecurityhasStorageRoomprice
0f8524536d4b09a0c8ccc0197ec9d7bdeszczecinblockOfFlats63.003.04.010.01980.053.37893314.6252966.539.00.1181.3890.6280.1051.652NaN0.413condominiumconcreteSlabNaNyesyesyesnoyes415000
1accbe77d4b360fea9735f138a50608ddszczecinblockOfFlats36.002.08.010.0NaN53.44269214.5596902.1516.00.2730.4920.6520.2910.3481.4040.205cooperativeconcreteSlabNaNnoyesyesnoyes395995
28373aa373dbc3fe7ca3b7434166b8766szczecintenement73.023.02.03.0NaN53.45222214.5533333.249.00.2750.6720.3670.2460.3001.8570.280condominiumbrickNaNnonononono565000
30a68cd14c44ec5140143ece75d739535szczecintenement87.603.02.03.0NaN53.43510014.5329002.2732.00.1750.2590.2230.3590.1010.3100.087condominiumbrickNaNyesyesnonoyes640000
4f66320e153c2441edc0fe293b54c8aebszczecinblockOfFlats66.003.01.03.0NaN53.41027814.5036114.071.00.2181.6900.5040.7040.5012.1380.514condominiumNaNNaNnonononono759000
52e190fcd6934978ca36d86ba41e842fcszczecinblockOfFlats63.303.02.04.01997.053.46310014.5728004.4810.00.0791.2240.7370.2601.1020.3770.745cooperativeconcreteSlabNaNyesyesnonoyes499000
6ec27024bfcd012728617a35dad2cb6b8szczecinblockOfFlats47.452.02.010.01974.053.45023214.5626252.9918.00.3270.3780.2340.2620.2441.7360.277condominiumconcreteSlablownonoyesnoyes370000
7d3e0e36529df3360849ec40168c10755szczecinapartmentBuilding60.082.03.04.02009.053.45468514.5515203.538.00.5720.7840.3050.4350.2571.9450.155condominiumbrickpremiumnoyesyesnono629000
87e1981e920d763d6237c5bdcf13cf5b7szczecinblockOfFlats47.762.08.012.01980.053.45886914.5364034.276.00.3450.5200.3360.5000.2651.8790.420condominiumconcreteSlabNaNnoyesyesnoyes399000
94a04a9c54d8281e3ec23df031e538d85szczecintenement72.094.02.03.01890.053.43509214.5596121.3022.00.2320.2920.3560.3010.1990.6530.199condominiumbricklowyesnononoyes325000
idcitytypesquareMetersroomsfloorfloorCountbuildYearlatitudelongitudecentreDistancepoiCountschoolDistanceclinicDistancepostOfficeDistancekindergartenDistancerestaurantDistancecollegeDistancepharmacyDistanceownershipbuildingMaterialconditionhasParkingSpacehasBalconyhasElevatorhasSecurityhasStorageRoomprice
68884204c93689b7cdc62a17ef3c0dbf7034abydgoszcztenement98.003.0NaN3.01925.053.12465518.0084590.5949.00.1130.2260.2250.4120.1240.4780.273condominiumbrickNaNnonononoyes550000
68885152dbb59ed1105801ae7d23cda0a64f3bydgoszczNaN68.002.0NaNNaNNaN53.13262017.9939901.2625.00.0751.1760.2260.1410.0390.3690.062condominiumNaNNaNnonoNaNnono335000
688865dba11f1621b6d9d05bd3d7150aac64abydgoszcztenement147.006.01.03.0NaN53.12600018.0079000.6554.00.1110.2050.3510.4050.0580.6220.205condominiumbricklownonononoyes520000
6888773d419e114a685f2695836168a127cb5bydgoszczapartmentBuilding91.004.02.02.02015.053.12968918.0426842.9213.00.1640.9880.3640.3040.3250.2820.340condominiumbrickNaNyesyesyesnono1000000
68888bb19da639a2de8bba49be2ca49053c87bydgoszcztenement108.965.02.04.01889.053.13174818.0006481.0828.00.2810.7920.2330.1760.1070.2260.143condominiumbrickNaNnonononoyes795000
688896a4b30b5fcdee00bfe5bcc0da82df9b1bydgoszczblockOfFlats94.304.0NaN2.01998.053.11592117.9563963.049.00.3782.2730.6050.1170.0880.7590.428condominiumNaNNaNnoyesnonoyes795000
688901e7f4f1fdfea31eb84e071d697839632bydgoszczNaN50.122.01.01.0NaN53.12965718.0038880.8842.00.1740.4960.3880.4600.0790.3320.250condominiumbrickNaNyesnononono360000
6889160fcbfa2a2a48ebcc2e554efba7f2729bydgoszcztenement59.102.01.01.01910.053.12600018.0079000.6554.00.1110.2050.3510.4050.0580.6220.205condominiumbrickNaNyesnononoyes320000
68892cd0241b70b79aaaf767a0dd3a7cfbb31bydgoszczNaN81.075.01.04.02019.053.11592117.9563963.049.00.3782.2730.6050.1170.0880.7590.428condominiumNaNNaNnoyesyesnono679000
688931cdf62d567c6be2fa488f16ad9939c3abydgoszczblockOfFlats133.164.03.03.0NaN53.11208617.9899451.329.00.1800.5810.4640.1770.3201.1830.319condominiumbrickpremiumnoyesnonoyes925000

Duplicate rows

Most frequently occurring

idcitytypesquareMetersroomsfloorfloorCountbuildYearlatitudelongitudecentreDistancepoiCountschoolDistanceclinicDistancepostOfficeDistancekindergartenDistancerestaurantDistancecollegeDistancepharmacyDistanceownershipbuildingMaterialconditionhasParkingSpacehasBalconyhasElevatorhasSecurityhasStorageRoomprice# duplicates
000099201961c051e30735cb48ecc4f4alodzblockOfFlats45.603.01.02.01940.051.77811019.5328605.396.00.3202.3410.1860.0811.9951.1950.248condominiumbrickNaNnoyesnonoyes2940004
3000f9c10ab7bab5e21b0409df27145a5gdanskblockOfFlats61.803.08.010.01976.054.39542018.5980306.2915.00.1181.1170.1320.2420.4371.9320.245condominiumconcreteSlabNaNnoyesyesnoyes7990004
6001d4c9e0eba3c22d54e87772a0e0001katowicetenement84.903.02.03.0NaN50.25960019.0247000.3690.00.0370.0310.3580.3130.0870.1830.244condominiumbrickNaNnonononoyes6600004
7002187c7e7ea5feefd2ac4ae181ef825bydgoszcztenement109.724.02.03.0NaN53.12348018.0084380.5454.00.1790.3290.1310.3530.1910.3500.389condominiumbrickNaNyesyesnonoyes8800004
9002cfe174b59701f43ce612e65f0e786gdanskblockOfFlats31.001.05.017.02021.054.39325018.6446204.9212.00.0630.1701.3020.1290.2682.2460.242condominiumNaNNaNnoyesyesnono4590004
120045a7dca9dcdd18d5468f8723ff02dbkrakowNaN123.004.01.02.02010.050.04305019.9167002.072.00.5781.2341.0890.3280.5180.1400.631condominiumbrickNaNnoyesnonono15500004
140059c9f27f15824e806530450dbb7e99gdanskapartmentBuilding48.102.03.04.02022.054.40171018.6328305.992.00.3720.9800.5560.3500.7122.8150.544condominiumbrickNaNnoyesyesyesno9247954
18006b23bae513388e246eb24d1006aea1krakowblockOfFlats64.493.03.03.02008.050.00074019.8942986.845.00.3520.9770.2340.2100.2304.6000.177condominiumNaNNaNyesyesnonoyes6490004
190070858c2aeff6ca1aa94cfa99289132bialystokblockOfFlats58.603.04.011.01984.053.13090023.1007002.989.00.4491.1590.1980.4090.4041.7720.212condominiumconcreteSlabNaNyesyesyesnoyes3790004
3100a03fe0042c3b417f9b11bb77893e21warszawaNaN79.003.04.04.02020.052.13785421.02912310.583.00.7540.7030.7060.1190.5011.1390.425condominiumbrickNaNyesnoyesyesno18000004